

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
__snake_case				:      Any   		=			""""""
__snake_case				:      Optional[int]   		=			""""""
__snake_case				:      Dict   		=			""""""
__snake_case				:      Dict   		=			1  # (0 is vertical, 1 is horizontal)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dataset(a__  ,	a__)
			print("""Processing...""")
			a_							,					a_							,					a_						:						str 							= update_image_and_anno(a__  ,	a__  ,	a__)

			for index, image in enumerate(a__):
						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						a_						:						List[Any] 							= random_chars(3_2)
						a_						:						Tuple 							= paths[index].split(os.sep)[-1].rsplit("""."""  ,	1)[0]
						a_						:						Optional[Any] 							= f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
						cva.imwrite(f'''/{file_root}.jpg'''  ,	a__  ,	[cva.IMWRITE_JPEG_QUALITY, 8_5])
						print(f'''Success {index+1}/{len(a__)} with {file_name}''')
						a_						:						List[str] 							= []
						for anno in new_annos[index]:
									a_						:						List[str] 							= f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
									annos_list.append(a__)
						with open(f'''/{file_root}.txt'''  ,	"""w""") as outfile:
									outfile.write("""\n""".join(line for line in annos_list))


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= []
			a_						:						Tuple 							= []
			for label_file in glob.glob(os.path.join(a__  ,	"""*.txt""")):
						a_						:						Any 							= label_file.split(os.sep)[-1].rsplit("""."""  ,	1)[0]
						with open(a__) as in_file:
									a_						:						Union[str, Any] 							= in_file.readlines()
						a_						:						List[Any] 							= os.path.join(a__  ,	f'''{label_name}.jpg''')

						a_						:						Optional[Any] 							= []
						for obj_list in obj_lists:
									a_						:						Union[str, Any] 							= obj_list.rstrip("""\n""").split(""" """)
									boxes.append(
									    [
									        int(obj[0]),
									        float(obj[1]),
									        float(obj[2]),
									        float(obj[3]),
									        float(obj[4]),
									    ])
						if not boxes:
									continue
						img_paths.append(a__)
						labels.append(a__)
			return img_paths, labels


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__ = 1):

			'''simple docstring'''



			a_						:						List[Any] 							= []
			a_						:						Union[str, Any] 							= []
			a_						:						int 							= []
			for idx in range(len(a__)):
						a_						:						Tuple 							= []
						a_						:						Optional[int] 							= img_list[idx]
						path_list.append(a__)
						a_						:						List[Any] 							= anno_list[idx]
						a_						:						Union[str, Any] 							= cva.imread(a__)
						if flip_type == 1:
									a_						:						List[str] 							= cva.flip(a__  ,	a__)
									for bbox in img_annos:
												a_						:						Dict 							= 1 - bbox[1]
												new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
						elif flip_type == 0:
									a_						:						Any 							= cva.flip(a__  ,	a__)
									for bbox in img_annos:
												a_						:						Tuple 							= 1 - bbox[2]
												new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
						new_annos_lists.append(a__)
						new_imgs_list.append(a__)
			return new_imgs_list, new_annos_lists, path_list


def 					_UpperCAmelCase				(						a__ = 3_2):

			'''simple docstring'''



			assert number_char > 1, "The number of character should greater than 1"
			a_						:						Dict 							= ascii_lowercase + digits
			return "".join(random.choice(a__) for _ in range(a__))


if __name__ == "__main__":
	main()
	print("""DONE ✅""")





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


